Martin Mundt
Full Professor at University of Bremen
Bremen, Bremen, Germany
Summary
👤
Senior🎓
Top SchoolMartin Mundt is a Full Professor and leader in continual and open-world lifelong machine learning with nine years of research experience bridging physics, computational neuroscience, and deep learning. He heads the OWL-ML group and has led independent research at TU Darmstadt and hessian.AI while serving on the board of the non-profit ContinualAI, reflecting strong community and interdisciplinary engagement. His background—MSc in Physics and a PhD in Computer Science—drives a rigorous, cross-disciplinary approach to robust neural networks and continual learning in computer vision. Known for practical, reproducible research, he has built research demonstrators and taught hands-on ML courses, translating theory into deployable systems. Based in Bremen, he combines academic leadership with active involvement in the broader ML community and a genuine passion for languages that informs collaborative, diverse teams. An under-the-radar strength is his ability to fuse computational neuroscience insights with scalable deep learning methods to tackle open-world robustness.
8 years of coding experience
6 years of employment as a software developer
PhD Computer Science, PhD Computer Science at Goethe University Frankfurt
English, German, Polish, French, Spanish, Japanese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (47)
generative10
classifiers10
denoising9
re-identification9
variational-autoencoder9
multi-target9
adversarial-learning9
arxiv9
uncertainty8
visual-recognition8
computer-vision8
probabilistic8
torchvision8
ensembles8
meta-learning8
Programming languages (3)
Github contributions (5)
PyMLVizard/PyMLViz
Sep 2018 - Nov 2019
Interactive machine learning algorithm visualisation using Python Jupyter notebooks
Contributions:8 commits, 3 PRs, 16 pushes in 1 year 2 months
jupyter-pythonpythonvisualisationdata-sciencealgorithm-visualisation
MrtnMndt/OpenVAE_ContinualLearning
May 2019 - Apr 2022
Open-source code for our paper: Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Contributions:11 commits, 1 PR, 14 pushes in 2 years 10 months
pytorchreplaydeep-learningrecognitioncontinual-learning
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